Since the introduction of the Mycin system more than 25 years ago, it has widely been hypothesized that extensive, well-represented computer knowledge-bases will facilitate a wide variety of scientific and clinical tasks. Driven by growing knowledge-management challenges adsing from the proliferation of high- throughput instrumentation, recently created knowledge-bases in areas related to genomics and related aspects of contemporary biology, such as the Gene Ontology, EcoCyc and PharmGKB, have begun to become integrated into the laboratory practices of a growing number of molecular biologists. However, these successful molecular biology knowledge-bases (MBKBs) have two drawbacks which impede their more general application: each has been narrowed to a particular special purpose, either in its domain of applicability or in the scope of knowledge represented, and each of these knowledge-bases was constructed largely on the basis of enormous human effort. Given the current state of molecular biology data and recent iadvances in database integration and information extraction technology, we proposed to test the following hypothesis: Current computational technology and existing human-curated knowledge resources are sufficient to build an extensive, high-quality computational knowledge-base of molecular biology. To test this hypothesis we propose to first create tools which can (a) automatically link incommensurate knowledge sources using semantic linking, and (b) use natural language processing techniques to extract new information from NCBrs GeneRIFs and from the GO definitions fields; and second, to evaluate the results of these methods by carefully quantifying the degree to which the induced linkages and extracted assertions are complete, consistent and correct. Although we propose to construct a broad and rich knowledge-base in order to develop and test the adequacy of largely automated methods to leverage existing human-curated collections, we do not propose to build a comprehensive MBKB.